ReviewJournal of cheminformatics2020
A review of optical chemical structure recognition tools.
Review in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed.
- Fine-Tuning DeepSeek-OCR‑2 for Molecular Structure Recognition.ACS omega · 2026Article
- A Benchmark Evaluation of Chemical Structure Extraction from Patents: Insights and Challenges in Chemical Structure Recognition.Chemical research in toxicology · 2026Article
- Practical Use of Advanced AI Frameworks on Real-Life Scientific Problems: Three Case Studies.bioRxiv : the preprint server for biology · 2026Article
- AdaptMol: domain adaptation for molecular image recognition with limited supervision.Journal of cheminformatics · 2026Article
- AI-assisted interpretation of Markush structures in pharmaceutical patents: a review of emerging tools, datasets, and challenges.Journal of cheminformatics · 2026Review
- Evaluating large language models on multimodal chemistry olympiad exams.Communications chemistry · 2025Article
- Machine-Readable Structural Information Is Essential for Natural Products Research.Journal of natural products · 2025Review
- FAIR and Effective Communication of Data on Chemical Contaminant Biotransformation in the Environment.Environmental science & technology letters · 2025Review
- MolNexTR: a generalized deep learning model for molecular image recognition.Journal of cheminformatics · 2024Article
- Automation and machine learning augmented by large language models in a catalysis study.Chemical science · 2024Review
- Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture.Journal of cheminformatics · 2024Article
- MMSSC-Net: multi-stage sequence cognitive networks for drug molecule recognition.RSC advances · 2024Article
- Ilm-NMR-P31: an open-accessJournal of cheminformatics · 2023Article
- YoDe-Segmentation: automated noise-free retrieval of molecular structures from scientific publications.Journal of cheminformatics · 2023Article
- HD_BPMDS: a curated binary pattern multitarget dataset of Huntington's disease-targeting agents.Journal of cheminformatics · 2023Article
- DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications.Nature communications · 2023Article
- Molecular Identification from AFM Images Using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks.ACS applied materials & interfaces · 2023Article
- Article
- MolMiner: You Only Look Once for Chemical Structure Recognition.Journal of chemical information and modeling · 2022Article
- Review of techniques and models used in optical chemical structure recognition in images and scanned documents.Journal of cheminformatics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structural information about chemical compounds is typically conveyed as 2D images of molecular structures in scientific documents. Unfortunately, these depictions are not a machine-readable representation of the molecules. With a backlog of decades of chemical literature in printed form not properly represented in open-access databases, there is a high demand for the translation of graphical molecular depictions into machine-readable formats. This translation process is known as Optical Chemical Structure Recognition (OCSR). Today, we are looking back on nearly three decades of development in this demanding research field. Most OCSR methods follow a rule-based approach where the key step of vectorization of the depiction is followed by the interpretation of vectors and nodes as bonds and atoms. Opposed to that, some of the latest approaches are based on deep neural networks (DNN). This review provides an overview of all methods and tools that have been published in the field of OCSR. Additionally, a small benchmark study was performed with the available open-source OCSR tools in order to examine their performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.